Trading in the stock market is fundamentally about making decisions based on data. In recent years, machine learning and deep learning technologies have significantly impacted the field of algorithmic trading, allowing investors to maximize their returns by leveraging these technologies. This course will focus on the Engle-Granger two-step method, explaining how to implement trading strategies through machine learning and deep learning.
1. Overview of Machine Learning and Deep Learning
Machine learning is a technology that allows computers to learn and make predictions from given data. On the other hand, deep learning is a subset of machine learning that builds complex models based on artificial neural networks to achieve higher accuracy. Both are suitable for algorithmic trading, but each has its own advantages and disadvantages.
1.1 Basic Concepts of Machine Learning
- Supervised Learning: A method where the model learns to predict new data based on input data and corresponding output data (labels) provided.
- Unsupervised Learning: A method where only input data is provided without output data, aimed at finding patterns or clusters among the data.
- Reinforcement Learning: A method that learns the optimal policy by interacting with the environment to maximize rewards.
1.2 Introduction to Deep Learning
Deep learning utilizes artificial neural networks composed of numerous layers, excelling at processing unstructured data (e.g., images, text). One of the main algorithms used in this area is Convolutional Neural Networks (CNN), while Recurrent Neural Networks (RNN) are used for processing sequence data.
2. Engle-Granger Two-Step Method
2.1 Overview of Engle-Granger
The Engle-Granger method is a methodology particularly suitable for analyzing time series data to forecast financial data. Considering the nonlinearities and complexities of the stock market, this methodology can be very useful. The two steps of this method are as follows.
- Step 1: Decomposition of Time Series Data – Separating the trend, seasonality, and irregular components of the data to examine each element.
- Step 2: Predictive Modeling – Building predictive models based on the decomposed data and applying machine learning/deep learning algorithms to forecast future price movements.
2.2 Step 1: Decomposition of Time Series Data
Time series data is data collected over time, which can be analyzed for patterns, trends, and periodicity. Using the Engle-Granger method, data is decomposed through the following procedures.
- Data Collection: Data can be collected from services like Yahoo Finance API, Google Finance API, or other data providers.
- Data Preprocessing: Improving data quality through handling missing values and detecting outliers.
- Data Decomposition: Analyzing the time series data divided into trend, seasonality, and irregular components using Additive or Multiplicative models.
2.3 Step 2: Predictive Modeling
Based on the decomposed data, a predictive model is built utilizing machine learning or deep learning. Basic algorithms such as ARIMA (model), LSTM, GRU, etc., can be employed to maximize performance.
2.3.1 LSTM (Long Short-Term Memory)
LSTM is a deep learning model that is highly useful for time series data prediction, with excellent capabilities for learning long-term dependencies. A basic LSTM network is structured as follows.
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout
# Preparing data
data = pd.read_csv("stock_prices.csv")
X, y = prepare_data(data)
# Building LSTM model
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(X.shape[1], X.shape[2])))
model.add(Dropout(0.2))
model.add(LSTM(50, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(1))
# Compiling and training the model
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(X, y, epochs=100, batch_size=32)
3. Implementing Algorithmic Trading Strategies
3.1 Data Collection and Preprocessing
Data collection is the first step in algorithmic trading, where it is essential to secure accurate and reliable data. Stock price data can be collected through data provider services like Yahoo Finance, Alpha Vantage, and Quandl.
3.1.1 Example of Data Collection
import yfinance as yf
# Downloading data
ticker = "AAPL"
data = yf.download(ticker, start="2020-01-01", end="2023-01-01")
data.to_csv("AAPL_stock_data.csv")
3.2 Feature Selection and Model Training
Feature selection is a crucial process that affects model performance. Features such as price, volume, and technical indicators can be extracted from historical data. A process of training and validating the machine learning model based on this is necessary.
3.2.1 Example of Technical Indicators
# Calculating moving averages data['SMA_50'] = data['Close'].rolling(window=50).mean() data['EMA_50'] = data['Close'].ewm(span=50, adjust=False).mean()
3.3 Model Evaluation
In the model evaluation stage, the training data and test data are separated to measure the model’s performance. Metrics such as RMSE (Root Mean Square Error) and MSE (Mean Square Error) are used to assess prediction accuracy.
4. Conclusion
The Engle-Granger two-step method is a valuable methodology for effectively analyzing and forecasting time series data. By implementing algorithmic trading strategies using machine learning and deep learning techniques, investors gain opportunities to make data-driven strategic decisions. This course introduced the fundamental concepts of algorithmic trading through machine learning and deep learning and provided a detailed explanation of the Engle-Granger method. It is hoped that continuous learning and experience will allow for the development of trading strategies in the future.